Dense human body representations.
To fit the complicated shapes more faithfully, dense human body representations have been proposed, including volumetric space [1]}, occupancy field [2]}, [3]}, dense UV correspondence [4]}, [5]}, and 3D mesh [6]}, [7]}, [8]}, [9]}, [10]}.
Among these methods, I2L-MeshNet [8]} proposes an efficient heatmap representation to estimate human joints and vertices in the image space and root-relative depth axis.
It can fit the input images accurately since heatmaps preserve the spatial relationship in image features extracted by a convolutional neural network (CNN).
Nonetheless, even when certain body parts are not visible in the image, this model is designed to localize all the joints and vertices within the image frame.
We show that it can negatively affect the model performance and emphasize the importance of additional visibility information.
